System and method for performing wind forecasting

ABSTRACT

A system and method for performing novel wind forecasting that is particularly accurate for forecasting over short-term time periods, e.g., over the next 1-5 hours. Such wind forecasting is particularly advantageous in wind energy applications. The disclosed method is anchored in a robust physical model of the wind variability in the atmospheric boundary layer (ABL). The disclosed method approach leverages a physical framework based on the unsteady dynamics of earth&#39;s atmosphere, and drives forecasting as a function of previously-observed atmospheric condition data observed at the same location for which a wind forecast is desired.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of U.S. application Ser. No.15/557,610, filed Sep. 12, 2017, which is a U.S. national stageapplication of International Application No. PCT/US2016/024508, filedMar. 28, 2016, which claims the benefit of priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 62/139,083, filed Mar.27, 2015, the entire disclosures of each of which are herebyincorporated herein by reference.

STATEMENT OF GOVERNMENT INTEREST

This invention was made with government support under Grant No.AGS-1026636 awarded by the National Science Foundation. The governmenthas certain rights in the invention.

FIELD OF THE INVENTION

The present invention relates generally to the fields of weatherforecasting and energy supply management forecasting. More particularly,the present invention provides a system and method for performing windforecasting that produces short-term wind forecasts with increased ease,and with increased accuracy that may be used to advantage in energysupply management and other commercial applications.

BACKGROUND

Various systems and methods presently exist for weather, andparticularly wind, modeling. Such wind modeling can be used in a varietyof applications, for a variety of purposes. One exemplary application isuse in the context of the energy market, which involves forecasting of avariety of weather conditions, including wind, for the purpose offorecasting energy supply, managing energy demand, and otherwise forperforming power grid management. Wind energy is emerging as anabundant, financially competitive, and increasingly adopted source ofclean renewable energy. Wind is a fast-growing source of primary energyproduction in the US, partly due to the competitive cost ofwind-generated electricity, even without subsidies, compared toelectricity generated from fossil sources or nuclear power plants.

The wind's kinetic energy can be harnessed by turbines to producerotational mechanical energy that can be easily converted toelectricity. Nevertheless, the wind speed rises and falls in time,meaning that the wind farms cannot produce a steady rate of electricityall the time. This creates uncertainties in the output wind power, whichcan have significant impacts on the energy market, as well as on gridoperation. Therefore, forecasting the generated wind power, as well asbracketing the uncertainty associated with each forecast, have becomeinherent challenges for the management of wind farms. Accurate forecastsand uncertainty estimates are critical to keep the electric supply anddemand in proper and favorable balance, and to reduce the costly back-upgeneration that is constantly running to compensate for unforeseenpotential drops in renewable generation (this back-up conventionalgeneration is completely wasted if actual wind energy production doesnot fall below forecasts, while if actual wind production exceedsforecasts, this additional generated power is also wasted since it wouldbe too late to sell it in energy markets). If wind is to contributelarge fractions of worldwide electric production in the future, thecosts associated with its time variability and the impact of thisvariability on the reliability of electric supply will risesignificantly if it is not well predicted.

Medium-term (approximately 1 day ahead) and long-term (approximately 2weeks ahead) forecasts, as well as wind farm siting, are best done usingmeso-scale models of the atmosphere (numerical weather predictionmodels). However, these models have inherent shortcomings that limittheir accuracy for short-term (e.g., 12 hours or less in advance)forecasts. Backup generation capacity, which takes 30-90 minutes to beramped up, should therefore be kept on standby to quickly compensate forthe departure of the actual wind energy production from forecasts in thehour-ahead. Thus, operators need a reliable short-term (e.g., 2 hoursahead or less) forecast to ramp up generation in time, otherwise dropsin wind energy production cannot be compensated; demand exceeds supply;and brownouts or blackouts might ensue. If such accurate short-termforecasts are not available, backup generation must be kept running as asafety measure. As wind power penetration increases, the impact of thesevariability and uncertainty will increase to eventually limit theoverall performance of the power system and/or raise power costs bykeeping backup systems running at a higher-than-needed rate. Therefore,accurate short-term forecasts are becoming increasingly critical for theefficiency of the whole electric system.

Despite this pivotal role, both the forecasting of short-term windvariability as well as the implications of this variability on windintegration continue to rely on highly simplistic models, and this fieldis thus ripe and ready for innovative ideas. A widely used method forshort-term forecasting remains the conventional so-called “naïvepredictor” or “persistence model,” which assumes that the wind speedover the next several hours will be equal to the one observed over thelast hour. This provides an inaccurate forecast, as wind speeds changerapidly. Sudden drops (called ramps) in wind speeds cannot be capturedby this method and can thus compromise power supply reliability. Othershort-term forecasting models boast improvement over the persistencemodel by as little as 10%, and they typically do not provide informationabout the forecasted wind direction. This short-term forecasting errornot only influences the cost and reliability of the produced power, butalso has a significant impact on the maximum achievable wind powerpenetration above which the reliability of the supply is compromised.Current estimates of this maximum achievable penetration rely onsimplistic approaches; however, using real-world forecasts (and forecasterrors resulting for example from the use of the persistence model) inan accurate grid model has highlighted the problems that grid operatorswill face as wind penetration increases. Goals of 30 percent of energyfrom wind would require substantial investments in fossil backupgeneration if better forecasts were not available. Short-term (e.g.,hour-ahead) forecast errors are thus more critical than longer term onessince they guide the planning of gas turbines: gas turbines can beturned on and integrated into the grid in less than 1 hour and thereforeif the hour-ahead forecasts (hypothetically) have no errors then littlebackup generation is needed. On the other hand, for errors in thehour-ahead forecasts, some gas turbines or other sources need to be keptrunning to allow for fast real-time adjustment.

Accordingly, in the energy context, wind forecasting is relevant in thecontext of wind power forecasting, to estimate electrical energygeneration from wind-driven electric power generation equipment in windfarms. Wind power output varies as wind speed rises and falls. Hence,predicting the wind variability and uncertainty is a critical componentof managing the power systems to keep the electric supply and demand inproper and favorable balance. Such information can be used to manageenergy supply such that, for example, expected shortfalls fromwind-generated power can be compensated for by firing of fossil-fuel (orotherwise powered) power generation equipment, or conversely,fossil-fuel fired/other power generation equipment can be taken offlinein response to expected increases in wind-generated power.

Conventional wind forecasting, particularly in the energy industry,relies primarily upon models that provide useful day-ahead forecastsover the following period of 24 hours or more. More specifically in thecontext of wind power, wind farm operators typically obtain 24-hourforecasts from the National Weather Service or other sources usingcomputer models. These forecasts however often have errors that reducethe efficiency of the wind farm since they either underestimate oroverestimate the generated power, which is particularly problematic forthe short-term power generation planning. As user herein, the term“short-term” refers to following periods of less than 24 hours, and morespecifically over following periods of the next 12, 11, 10, 9, 8, 7, 6,5, 4, 3, 2 or 1 hours. Such inaccuracy in the short-term forecast oftenleads to wasted power or a power shortage, as discussed below.

To alleviate this problem, some wind farm operators use more accurateshort-term forecasts for the next 2 hours to plan their short-termoperations. These models are typically statistical models or a blend ofstatistical models with numerical weather prediction data. However,these short-term forecasts still have substantial errors and at bestoutperform the naïve persistence model very moderately (about 5-20%improvement) due to the absence of any physical anchors in most of thesedata-driven approaches. This limits the usefulness of such windforecasts, with respect to the energy industry as well as in othercontexts.

What is needed is a system and method for performing wind forecastingwith increased accuracy, and in particular for performing windforecasting with a high-degree of accuracy over short-term futureperiods.

SUMMARY

The present invention provides a system and method for performing windforecasting with increased accuracy for short-term future periods thatis beneficial to any application for which short-term wind forecasting,e.g., within the next 1-6 hours, is needed, such as in the context ofenergy supply management. The disclosed method benefits any applicationfor which accurate short-term wind forecasting is desired.

The method provides a historical data-driven, but physically-based,approach. It is based on the extrapolation from recent wind-speed dataof the slowly-varying large scale atmospheric pressure force gradients(rather than extrapolation of wind speed directly, as is currently thenorm for the short-term forecasting models) into the future, and thendetermining the future wind speed as a function of those extrapolatedpressure force gradients. This approach is advantageous in that itprovides particularly accurate short-term wind forecasts in part becausewind speeds are highly variable over time, while the large-scaleatmospheric pressure forces vary more slowly over time. By accountingfor a recent trend, the large-scale atmospheric pressure forces can beextrapolated into a future period to provide an accurate large-scaleatmospheric pressure force forecast for the future period. Using themodel, a corresponding wind speed forecast can be determined for thefuture period with increased accuracy over other wind forecastingmethods, particularly for a forecast for a short-term future timeperiod.

A primary application is in wind energy. The disclosed method isanchored in a robust physical model of the wind variability in theatmospheric boundary layer (ABL). The disclosed approach, by introducinga physical framework based on the unsteady dynamics of the atmosphere,and driving it with previous observations at the same location (ormultiple nearby locations), marks a departure from current practice andoffers an improved approach for short-term forecasting.

According to one aspect, the present invention provides acomputer-implemented method for performing wind forecasting using acomputer-implemented wind forecasting system comprising amicroprocessor, a memory operatively coupled to the microprocessor, andmicroprocessor-executable instructions for causing the wind forecastingsystem to perform the wind forecasting method. In one exemplaryembodiment, the method comprises: storing, at the wind forecastingsystem, a physical model of time-varying wind flow in earth'satmosphere, the model correlating atmospheric pressure forces to windspeeds over time; receiving, at the wind forecasting system, datareflecting changes over time of wind speeds for a particulargeographical region during a preceding period of time, the precedingperiod of time preceding a reference time; performing, at the windforecasting system, an inverse application of the model to determine, atthe wind forecasting system, a trend reflecting changes over time oflarge-scale atmospheric pressure forces for the geographical regionduring the preceding period of time as a function of the changes overtime in wind speeds for the geographical region; extrapolating, at thewind forecasting system, the trend to determine forecasted large-scaleatmospheric pressure forces for the geographical region during a futureperiod (e.gt., short-term period) of time, the future period of timesucceeding the reference time; and performing, at the wind forecastingsystem, a forward application of the model to determine, at the windforecasting system, forecasted wind speeds for the geographical regionduring the future period of time, as a function of the forecastedlarge-scale atmospheric pressure forces for the future period of time.

BRIEF DESCRIPTION OF THE FIGURES

An understanding of the following description will be facilitated byreference to the attached drawings, in which:

FIG. 1 is a diagram showing an exemplary networked computing environmentincluding a wind forecasting system in accordance with an exemplaryembodiment of the present invention;

FIG. 2 a schematic showing an exemplary power generation systemincluding a wind forecasting system in accordance with an exemplaryembodiment of the present invention;

FIG. 3 is a flow diagram illustrating an exemplary method for performingwind forecasting in accordance with an exemplary embodiment of thepresent invention;

FIGS. 4A and 4B are graphs showing comparisons, for two different timeperiods, of actual observed wind speeds to wind speed forecasts for aconventional prior art persistence model in comparison to the inventiveforecasting method;

FIG. 5 shows graphs showing a comparison of the accuracy of the windspeed forecasts made using the invention method and the persistencemodel, as illustrated by the probability density function of theabsolute errors; and

FIG. 6 is a schematic showing an exemplary wind forecasting system inaccordance with an exemplary embodiment of the present invention.

DETAILED DESCRIPTION

The present invention provides a system and method for wind powerforecasting that leverages a dynamic wind modeling analysis to providewind forecasts that provide improved accuracy in short-term windforecasts. The method provides a historical data-driven, butphysically-based, approach. It is based on the extrapolation from recentwind-speed data of the slowly-varying large scale atmospheric pressureforce gradients (rather than extrapolation of wind speed directly, as iscurrently the norm for other available models, including the persistencemodel) into the future, and then determining the future wind speed as afunction of those extrapolated pressure force gradients. The presentinvention can significantly reduce errors in short-term wind forecasts.This results in significant improvement to conventional short-term windpredictions. Compared to conventional predictions, this model is easierto generalize and more accurate than statistical approaches, and muchfaster than fully 3D numerical weather prediction models, withsignificant reductions in the short-term prediction errors compared toexisting statistical as well as dynamical approaches.

The model is anchored in a robust physical model of wind variability inthe atmospheric boundary layer (ABL). Current short-term forecastingapproaches are statistical or data driven, with little physical basis.Our model, by introducing a physical framework based on the unsteadydynamics of the atmosphere, and driving it with previous observations atthe same geographical location, departs from prior practice and providesa novel approach for short-term forecasting.

For illustrative purposes, exemplary embodiments of the presentinvention are discussed below with reference to FIGS. 1-6. FIG. 1 is aschematic diagram showing an exemplary networked computing environment10 including a wind forecasting system (WFS) 200 in accordance with anexemplary embodiment of the present invention. In this exemplaryembodiment, the WFS 200 is operatively connected to other computingsystems via a communications network 40, such as the Internet and/or aVirtual Private Network (VPN) connection. Hardware and software forenabling communication of data via communications networks are wellknown in the art and beyond the scope of the present invention, and thusare not discussed in detail herein.

Further, the computing environment 10 further includes a weather datasource 50 that is operatively connected for communication via thenetwork 40. Accordingly, in this embodiment, the WFS 200 is capable ofreceiving weather data from the weather data source 50 via thecommunications network 40. However, in alternative embodiments, theweather data may be communicated to the WFS 200 by other means, e.g., bystoring the weather data on tangible computer readable media andphysically transporting such media to the WFS 200 where it may be readso that the weather data may be retrieved therefrom. The weather datasource may be any proprietary or conventional commercially availabledata source. By way of example, wind data from national weather system,satellite data, airline reports, wind farm operator's data, or publicdata such as buoy data from National Oceanic and AtmosphericAdministration may be used as the weather data source. Any suitableweather data information may be provided by the weather data source. Inparticular, and in accordance with the present invention, the weatherdata source stores and transmits to the WFS 200 weather data includingtime-dependent wind speed data for one or more particular geographicalregions. Such wind speed data may include, for example, data reflectingchanges over time of wind speeds, e.g., as a time-dependent horizontalwind velocity vector (e.g., wind speed and direction), for a particulargeographical region during a preceding period of time. For example, thewind speed data may consist of one or more time series for the windspeed and direction, acquired every 1 to 10 minutes, during thepreceding days, weeks or months.

Although the systems for wind forecasting technology described hereinhave many applications, such as for wind speed forecasting aroundairports or in consumer-oriented electrics, of particular interest isthe use of the wind forecasting technology described herein inconnection with the electrical power field. The use of the novel windforecasting technology can improve the operational efficiency of windfarms, electricity market clearing, economic load dispatch planning,generation and load increment/decrement decisions, and the regulatoryframework for wind energy, therefore reducing the cost of wind energyproduction. For this reason, it can have a transformative impact on theability to handle variability and uncertainty of wind. Its incorporationinto an energy market model will open new possibilities for optimizinggrid integration. More specifically, this novel wind forecastingsignificantly reduces errors in short-term forecasting and will hencereduce the financial overhead related to backup power generation (whenthe actual wind is less than forecasted wind) and unused generated windpower (when the actual wind is greater than forecasted wind). This willresult in large financial savings for wind operators and the grid as awhole, and increase the capacity factor for wind farms.

It is believed that a 20% penetration of wind requires over 3% of thepower generation capacity running purely in reserve. In other words,high wind penetration would require a backup generation of about 15% ofthe power that wind will produce (and that fraction increases ifpenetration is above 20%). This needed backup generation is estimated tofall from 20% to 3% of the produced wind energy if one could totallyeliminate forecast uncertainties. Alternatively, substantial investmentsin expensive storage could be used if storage technologies evolve, butthis storage need also largely vanishes if short-term forecastinguncertainties can be eliminated.

FIG. 2 is a schematic diagram showing an alternative networked computingenvironment in the nature of a power generation system 300 forgenerating electrical power supplied to a power grid. For illustrativesimplicity, FIG. 2 shows primary power generation equipment 320 poweredby a source of wind. This primary power generation equipment 320 can bethat of a conventional electrical power-generating wind farm (even assmall as a single turbine), including onshore and offshore wind farms,or even multiple farms and wind energy producing devices that areinterconnected over a region and operated or managed together to supplyenergy to an interconnected grid, as long as a short term forecast isused to optimize the functioning of the wind energy generating devices.Such equipment is well known in the art and beyond the scope of thepresent invention, and thus is not discussed in greater detail herein.The primary power generation equipment may be physically disposed in aparticular geographical region, for example, within a particular town,country, etc., or may be part of an interconnected grid covering large,less-well-defined areas, such as the eastern United States.

Further, FIG. 2 shows secondary power generation equipment 340 poweredby an energy source other than wind. This secondary power generationequipment 340 can be that of a conventional, fossil fuel-powered,nuclear-powered, hydroelectric, or other electrical power generationfacility, the production of which can be ramped up or down to compensatefor fluctuations in the produced wind energy, and which is thereforecontrolled jointly with the wind energy producing turbines or farms.Such equipment is well known in the art and beyond the scope of thepresent invention, and thus is not discussed in greater detail herein.

As is well-known in the field of electrical power generation, theoperation and power output for such power generation systems isregulated and/or controlled to produced desired amounts of electricalpower to meet grid/other power demands by a control system 360.Conventional control systems are well known in the art and beyond thescope of the present invention, and thus are not discussed in greaterdetail herein.

In accordance with the present invention, the electrical powergeneration system 300 further includes a wind forecasting system 200,which is shown in the exemplary system of FIG. 2 for illustrativeclarity as a system separate and distinct from the control system 360.As described herein, the wind forecasting system 200 performs andcommunicates wind forecasts, including wind speed forecasts that areparticularly accurate in the short-term. In accordance with thisexemplary embodiment of the present invention, those forecasts areprovided to the control system 360. The control system 360 uses suchforecasts to control operation of the primary and second powergeneration systems 320, 340 in an otherwise conventional matter. Inother embodiments, the essential functionality of the WFS 200 may beincorporated into the control system 360 to provide a novel controlsystem 360. In either case, the control system 360 receives forecastedwind speeds for the geographical region of interest (e.g., correspondingto a geographical location of a single turbine, a single wind farm,multiple wind farms, an airport, etc.) during the future period of time,the control system responsively transmits a control signal to thesecondary power generation equipment to throttle (by increasing ordecreasing, as appropriate) electrical power production from thesecondary power generation equipment as a function of the expectedoutput from the primary power generation equipment, which is a functionof the forecasted wind speeds. This transmitted control signal does nothave to be a fully-automated or electronic signal, and could consist ofa human-produced message if the control system is built such as toinclude a certain level of human control over the operation.

Referring now to FIG. 3, a flow diagram 400 is shown that illustrates anexemplary method for performing wind forecasting in accordance with anexemplary embodiment of the present invention. In this exemplaryembodiment, wind forecasting is performed by a WFS 200, which includes aprocessor, a memory operably connected to the processor for storinginstructions, and instructions stored in the memory for causing thesystem to carry out the wind forecasting method, as discussed in greaterdetail below with reference to FIG. 6.

In alternative embodiments of the present invention, all of the portionsof the exemplary method may be carried out by a WFS 200 in combinationwith a network-connected device (such as a weather display device 30 aor smartphone 30 b), external system 100 and/or control system 360, orby a weather display device 30 a, 30 b, external system 100, or controlsystem 360, each of which may include hardware and software of a typediscussed below with reference to the WFS 200 in FIG. 6.

Referring again to FIG. 3, the method begins with storing, e.g., in thememory of the WFS 200, a model of time-varying wind flow in the earth'satmosphere, as shown at step 402 of FIG. 3. The model correlatesatmospheric pressure forces to wind speeds over time for a geographicalregion. The large-scale atmospheric pressure forces may be expressed asan equivalent geostrophic wind. Any suitable model may be used for thispurpose.

By way of non-limiting example, the model may be a mathematical modelrelating an imbalance among an atmospheric pressure gradient, Coriolisforce (Coriolis frequency) and turbulent stress resulting from turbulentair flow mixing in the earth's atmosphere for a particular geographicalregion to changes over time in Zonal and Meridional horizontal windspeeds for the geographical region. Accordingly, the model may correlatelarge-scale atmospheric pressure forces to time-dependent horizontalwind speed, or wind velocity vectors, for a particular geographicalregion.

By way of further non-limiting example, in one embodiment themathematical model comprises equations of an unsteady atmospheric (Ekmanor otherwise) boundary layer relating changes over time in the Zonal andMeridional wind speeds to the imbalance between the pressure gradientforce, the Coriolis force and the friction force (mainly caused byturbulent eddy mixing causing turbulent stresses). In one illustrativeand non-limiting example of such a mathematical model, the mathematicalmodel comprises the expression:

${{\frac{d^{2}A}{{dt}^{2}} + {\alpha \frac{dA}{dt}} + {{f_{c}\left( {f_{c} - {i\alpha}} \right)}A}} = {{f_{c}^{2}A_{g}} + {{if}_{c}\frac{dA_{g}}{dt}}}},$

where

A is the time-dependent horizontal wind velocity vector expressed as acomplex variable (A=U+i V, where U and V are the Zonal and Meridionalhorizontal wind speed components, respectively) and A_(g) is ageostrophic value reflecting large scale atmospheric pressure force;f_(c) is the Coriolis force parameter; and α is a quantification ofturbulent eddy mixing induced by the turbulent eddy viscosity, which iswell known in the art and beyond the scope of the present invention. Byway of example, α may be determined as follows:

${{\alpha \left( {z,t} \right)} = {{{- \frac{1}{U}}\frac{\partial}{\partial z}\left( {v_{T}\frac{\partial U}{\partial z}} \right)} = {{- \frac{1}{V}}\frac{\partial}{\partial z}\left( {v_{T}\frac{\partial V}{\partial z}} \right)}}},$

where

z is the elevation above ground at which the wind velocity vector issought, U is the Zonal wind speed and V the Meridional wind speed (bothfunctions of elevation z and time t), and v_(T) the turbulent eddyviscosity, which is well known in the art.

By way of example, in the lowest 150 meters or so of the atmosphere αmay also be estimated as follows:

${{\alpha (z)} = {\frac{1}{\ln \left( {z/z_{0}} \right)}\frac{\partial}{\partial z}\left( \frac{v_{T}(z)}{z} \right)}},$

where

z is the elevation above ground at which the wind velocity vector issought, z₀ is the roughness length of the earth surface underneath, andv_(T) the turbulent eddy viscosity, which is well known in the art. Byway of alternative example, α may also be determined from priormeteorological observations at the geographical region/site of interest.

By way of example, the wind velocity used for wind-energy applications,could be obtained from the anemometers that measure the wind speed andits direction at wind turbines hub height (e.g. around 10-200 m abovethe ground) in the particular geographical location of interest, or evenat lower heights that subsequently can be extrapolated to hub heights.

In this manner, the wind forecasting is based upon a novel approach fordescribing unsteady wind dynamics (large-scale atmospheric pressurefields) near the earth's surface, which involves highly turbulent airflow dynamics in the lower atmosphere. The exemplary mathematical modeldescribed above provides a damped-oscillator model for the wind speeddynamics.

Next, the method involves receiving, e.g., at the WFS 200, datareflecting wind speeds for a specific geographical region, as shown atstep 404 in FIG. 3.

Accordingly, the received data may reflect time-dependent horizontalwind velocity vectors. These data are observed and reflect historicalwind speed measurement for the specific geographical region of interest;they could include datasets from a single or a network of observationalstations or remote sensing devices for the domain of interest.Accordingly, the data is for one or more specific geographical regionsduring a preceding period of time, the preceding period of timepreceding a reference time, which may be the current time or a recent,preferably as recent as possible, reference time. The preceding periodof time may be a short-term period of time, or a longer period of time.It may be advantageous to use data from a short-term period of timeparticularly when it is desired to forecast for a future short-termperiod of time, because the most relevant data is thereby used forforecasting purposes.

Accordingly, the preceding period of time may be a period of time of notmore than 10 hours, not more than 6 hours, not more than 5 hours, notmore than 4 hours, not more than 3 hours, not more than 2 hours, notmore than 1 hour, or of less than 1 hour. By way of non-limitingexample, the receiving, at the wind forecasting system, data may reflectchanges over time of Zonal and Meridional horizontal wind speeds for thegeographical region.

By way of example, this may involve receipt of data from a weather datasource 50 via a communications network 40, as shown in FIG. 1. By way offurther example, such data may be received as a computer data file orcontinuous stream of data in any format from wind-farm operators, nearbyweather stations, or other sources. Referring now to FIG. 6, the datareceived may be stored in the memory 218 of the WFS 200 for furtherprocessing as described herein.

Referring again to FIG. 3, the method next involves determiningaccording to the model, e.g., at the wind forecasting system, a trendreflecting changes over time of large-scale atmospheric pressure forcesfor the geographical region during the preceding period of time, asshown at step 406. Accordingly, with respect to the exemplarymathematical models described above, this may involve performing, e.g.at the wind forecasting system, an inverse application of the model todetermine the trend reflecting changes over time of large-scaleatmospheric pressure forces as a function of the changes over time inwind speeds (e.g., Zonal and Meridional horizontal wind speeds) for thegeographical region during the preceding period. In effect, thisinvolves using the wind speed data (A) as the input to the model, andsolving the equation to determine the corresponding large-scaleatmospheric pressure forces (A_(g)) for the same preceding period oftime, according to the model.

The method further involves extrapolating the trend, e.g., at the windforecasting system, to determine forecasted large-scale atmosphericpressure forces for the geographical region during a future period oftime, as shown at step 408 in FIG. 3. The future period of time is aperiod of time succeeding the reference time. In a preferred embodiment,because this method provides substantially more accurate windforecasting for short-term future periods of time, the future period oftime may be a period of time of not more than 10 hours, not more than 6hours, not more than 5 hours, not more than 4 hours, not more than 3hours, not more than 2 hours, not more than 1 hour, or of less than 1hour.

The extrapolation of the large-scale atmospheric pressure forcesinvolves determining expected/forecasted large-scale atmosphericpressure forces for a particular geographic region based on observedlarge-scale atmospheric pressure forces for the particular geographicregion during a recent time period preceding the future period for whichthe forecast is desired. This may involve determining a trend in therecently observed pressure force data, and extrapolating that trend intothe future in a manner accounting for the trend. Any suitable method maybe used for extrapolating the pressure forces to determine forecastedpressure forces.

The extrapolation of the trend may be performed using various methods.By way of example, the extrapolation of the pressure forces may beperformed using a simple linear extrapolation in time. In this example,a linear change for the pressure forces is extrapolated, such that atrend of change of the pressure forces over time is assumed to continuein the future in unchanged fashion.

Accordingly, the method further includes determining according to themodel, e.g. at the wind forecasting system, a forecasted wind speeds asa function of the forecasted large-scale atmospheric pressure forces, asshown at step 410. More specifically, this may involve determining atime-dependent horizontal wind velocity vector for the geographicalregion during the future period of time, as a function of the forecastedlarge-scale atmospheric pressure forces for the future period of time,according to the model. This determining may be achieved by performing aforward application of the model to determine forecasted wind speeds forthe geographical region during the future period of time, as a functionof the forecasted large-scale atmospheric pressure forces for the futureperiod of time. In effect, this involves using the forecastedlarge-scale atmospheric pressure forces (A_(g)), which are extrapolatedfrom a trend in recent data, for the future period of time, and solvingthe equation to determine forecasted wind speed data (A) for the futureperiod of time, according to the model.

This approach is advantageous in that it provides particularly accurateshort-term wind forecasts in part because wind speeds are highlyvariable over time, while the large-scale atmospheric pressure forcesvary more slowly over time. By accounting for a recent trend, thelarge-scale atmospheric pressure forces can be extrapolated into afuture period to provide an accurate large-scale atmospheric pressureforce forecast for the future period. Using the model, a correspondingwind speed forecast can be determined for the future period withincreased accuracy over other wind forecasting methods, particularly fora forecast for a short-term future time period.

By way of example, a first observational data set is provided from theCHLV2 station of the National Oceanic and Atmospheric Administration's(NOAA) Buoy Center that is located in the offshore of the VirginiaBeach. Its anemometer height is 43.3 m above the mean sea level and thedata includes wind speed and direction for a whole year (2012), every 10minutes. A second data set is provided by the NWTC 135-m tower of theNational Renewable Energy Laboratory located in Colorado, for April 2014wind speed and direction data provided every 10 minutes. FIGS. 4A and 4Bshow comparisons, for two different time periods of actual observed windspeeds to wind speed forecasts for a conventional prior art persistencemodel in comparison to the inventive forecasting method. FIG. 4Arepresents the test of the inventive model for the offshore wind data,while FIG. 4B displays the test for the onshore data.

Referring now to FIGS. 4A and 4B, the dotted lines are actual observedwind speed data. The straight lines show predicted wind speeds using aconventional prior art persistence model wind speed forecast. The curvedlines show predicted wind speeds using the inventive wind forecastingmethod disclosed herein. For each forecast, the bases of the forecastsare the “measured” wind speeds before the start of the predictionsimulation. As shown in these Figures, for both data sets, the novelforecasting method disclosed herein provides a more accurate windforecast, particularly over the short-term time periods, as compared toa conventional persistence model-based forecast.

Table 1 (below) shows error statistics based on the absolute model'serrors for one exemplary forecasting horizon, comparing actual observedwind speed to forecasted wind speed, for forecasts made using theconventional persistence model to forecasts made according to thepresent invention. As shown in this Table 1, the present inventionprovides lower relative forecast error, and a high fraction of periodswith improved wind speed forecast as compared to the persistence model,and thus shows a higher accuracy of wind speed forecasts.

TABLE 1 Present Persistence Invention Fraction of Improved Periodscompared — 88% to the persistence model (%) Average RMSE 1.05 (m/s) 0.53(m/s) 90 percentile Error 2.0 (m/s) 0.91 (m/s)

FIG. 5 shows graphs showing a comparison of the accuracy of the windspeed forecasts made using the invention method and the persistencemodel, as illustrated by the probability density function of theabsolute errors. More specifically, FIG. 5 compares the probabilitydensity functions (from many different time periods) of the error froman exemplary embodiment of the present (e.g., a Quadratic Optimized MassSpring Damper (OMSD) model) consistent with the present inventionagainst persistence model predictions. Dashed lines show the mean, and95-percentile error. Table 1 and FIG. 5 both illustrate that theinventive method outperforms the persistence model significantly: itreduces the average, relative, and 90th percentile errors of predictionsto less than half compared to persistence model errors. Moreover, itoutperforms persistence in about 90% the prediction periods.

Referring again to FIG. 3, the exemplary method further includestransmitting a control signal to control operation of a connected systemas a function of the forecasted wind speed, i.e., according to the windforecast, as shown in FIG. 412. For example, a control signal for aspecific wind power system as shown in FIG. 320, could include theforecasted generated wind power, which is a function of the forecastedwind velocity (which is correlated to a wind power forecast). This mayoccur in any suitable fashion, and the connected device may be adiscrete and separate computing hardware connected via a communicationsnetwork, or may be a connected component with a unitary computinghardware appliance or consumer product.

As noted above, wind forecasting in accordance with the presentinvention has many applications in many different fields. By way ofnon-limiting example, the novel wind forecasting methodology may be usedin connection with wind energy forecasting performed by TransmissionSystem Operators (TSOs) and Independent Power Producers (IPPs). By wayof example, the wind forecasting may be used for the purpose ofelectricity market clearing, economic load dispatch planning, loadincrement/decrement decisions, regulation actions and more. The novelwind forecasting methodologies can significantly reduce errors inshort-term wind forecasting, can allow for more efficient wind powergeneration, can provide more accurate and less computationally expensive(much faster) forecasts than current forecast models, such as thepersistent or other statistical models, and is anchored to a robustphysical model of the wind variability that makes it general andlocation-independent, so that it may be used to perform forecasts invarious different geographical regions.

Accordingly, for example, the wind forecasting functions may beincorporated into an electrical power generation system, as describedabove with reference to FIG. 2. Such a power generation system mayinclude not only a wind forecasting system as described herein, but alsoa control system operatively connected to the wind forecasting system,the control system receiving the forecasted wind speeds for thegeographical region during the future period of time, the control systemresponsively transmitting a control signal to the secondary powergeneration equipment to throttle electrical power production from thesecondary power generation equipment as a function of the forecastedwind speeds, the forecasted wind speeds corresponding to an expectedpower production from the primary power generation system.

Referring again to FIG. 1, the exemplary networked computing environment10 further includes computing devices operated by individual users,members of the general public, consumers, etc., such as weather displaydevice 30 a and web-enabled smartphone 30 b. Any suitable computingdevice may be used for this purpose. Further, the exemplary networkedcomputing environment 10 also includes a computing device operated by anorganization, such as a corporation or other business entities, weatherbureau, state/local/other governmental entity, etc., such as externalsystem 100. As described in further detail herein, the weather displaydevice 30, web-enabled smartphone 30 b or external system 100 receivesvia the communications network wind forecast information prepared by theWFS 200 based at least in part on the weather data received from theweather data source 50. Hardware and software for enabling web-based(and other) communication of data among networked computing devices arewell known in the art and beyond the scope of the present invention, andthus are not discussed in detail herein.

In other embodiments, the functionality of the WFS 200 may be integratedinto another system, to provide a unitary device, such thatcommunication between the devices 30 a, 30 b or external system 100occurs within a unitary device, rather than via a communicationsnetwork. For example, the essential functionality of the WFS 200 may beincorporated into a weather display device 30 a, such as a weatherstation for home use, such that the weather display device receives datafrom the weather data source 50 via the network 40, and then performsand displays the wind forecasts discussed herein. This may beaccomplished, for example, via hardware and/or software of the weatherdisplay device 30 a. By way of further example, the essentialfunctionality of the WFS 200 may be incorporated into the externalsystem, such as a control system for an electrical power generationsystem, such that the external system 100 receives data from the weatherdata source 50 via the network 40, and then performs and utilizes thewind forecasts discussed herein, e.g., by issuing control signals toregulate operation of the power generation system. Thus, the controlsignal may be transmitted to control operation of a dependent system asa function of the forecasted wind speed. Thus, the dependent system iscontrolled at least in part by a forecast determined by the windforecasting system. In one embodiment the dependent system is acomputerized device including a display for displaying a wind speedalert, and thus the control signal causes the display to display theforecasted wind speed. By way of example, this could be performed by aweather station for consumer/home use that is in network communicationwith the external system 100. Alternatively, the dependent system couldbe a computerized device for issuing a wind speed alert, and wherein thecontrol signal causes the device to issue the wind speed alert. By wayof example, the alert could be issued via a software “app” executing ona conventional smartphone, and in network communication with theexternal system 100.

Accordingly, the method may involve transmitting a control signal forcontrolling operation of a dependent system, such as the weather displaydevice 30, web-enabled smartphone 30 b or external system 100, as afunction of the forecasted wind speed during the future period of time,such that operation of the dependent system is controlled at least inpart by a wind forecast determined by the wind forecasting system.

By way of further example, the dependent system may be a powergeneration system, and the control signal may cause the power generationsystem to increase or decrease output from electrical power generationequipment as a function of the forecasted wind speed.

By way of further example, the WFS 200 or a discrete electronic devicemay include the WFS forecasting functionality and a display fordisplaying forecasted wind speeds. In this case, instructions stored inthe memory of the device include instructions for causing the display todisplay the forecasted wind speeds for the geographical region duringthe future period of time.

By way of further example, the WFS or a discrete electronic device mayfurther include a transmitter for transmitting a control signal, andinstructions may be stored in the memory for causing the transmitter totransmit a control signal indicating the forecasted wind speeds for thegeographical region during the future period of time. Alternatively, thedevice may include instructions for causing the transmitter to transmita control signal for issuing a wind speed alert from a computerizeddevice receiving the control signal, e.g., via a software app ofsmartphone device.

It will be appreciated, by way of further example, that the forecastingdescribed herein may be combined with other models or methods to producesystems that may have various applications. Any system that involves useof the present forecasting method is part of this description. By way ofexample, an exemplary system may comprise autonomous drones whereby thedrones use the predicted wind speed to optimize their flight plan andpath and/or simultaneously collect and provide wind data to theprediction system to improve its performance. Or they may compriseself-driving vehicles whereby the vehicles use the predicted wind speedoptimize their operation and performance and/or simultaneously collectand provide wind data to the prediction system to improve itsperformance. In addition, the present methods can be applied with datafrom a single or a network of air quality monitoring stations, togetherforming a system for predicting and analyzing future air qualityevolution for various air pollutants including but not limited toparticulate matter, volatile organic compounds (VOCs), NO, NO2, SO2, O3,NH3, and non-biological or biological particles including sand, dust,soil particles, microbes, fungal spores, bacteria spores, viruses,pollen, mite, remain of biological beings living or historical. Themethod may also be useful for predicting the evolution of naturalphenomena including but not limited to rain, snow, storm, and fire. Themethods may be combined with models for energy consumption of buildings,together forming a system for predicting and analyzing future energydemand and optimizing the building performance for comfort and cost. Themethod can be integrated with a model for powerline resilience, togetherforming a system for predicting and analyzing the probability of failurein powerlines to preemptively perform maintenance operations that reducefailure probability. The method can also be used for the simulation,prediction and analysis of air and space transportation including butnot limited to by airplanes, drones, rockets and other airtransportation, wind farms, buildings, powerlines, communication towers,highways, and rail roads.

FIG. 6 is a schematic diagram showing an exemplary wind forecastingsystem (WFS) 200 in accordance with an exemplary embodiment of thepresent invention. The WFS 200 is shown logically in FIGS. 1 and 2 as asingle representative server for ease of illustration only. The WFS 200includes conventional server hardware storing and executingspecially-configured computer software for carrying out a method inaccordance with the present invention. Accordingly, the exemplary WFS200 of FIG. 6 includes a general purpose microprocessor (CPU) 202 and abus 204 employed to connect and enable communication between themicroprocessor 202 and the components of the WFS 200 in accordance withknown techniques. The exemplary WFS 200 includes a user interfaceadapter 206, which connects the microprocessor 202 via the bus 204 toone or more interface devices, such as a keyboard 208, mouse 210, and/orother interface devices 212, which can be any user interface device,such as a touch sensitive screen, digitized entry pad, etc. The bus 204also connects a display device 214, such as an LCD screen or monitor, tothe microprocessor 202 via a display adapter 216. The bus 204 alsoconnects the microprocessor 202 to memory 218, which can include a harddrive, diskette drive, tape drive, etc.

The WFS 200 may communicate with other computers or networks ofcomputers, for example via a communications channel, network card ormodem 222. The WFS 200 may be associated with such other computers in alocal area network (LAN) or a wide area network (WAN), and may operateas a server in a client/server arrangement with another computer, etc.Such configurations, as well as the appropriate communications hardwareand software, are known in the art.

The WFS's software is specially configured in accordance with thepresent invention. Accordingly, as shown in FIG. 6, the WFS 200 includescomputer-readable, processor-executable instructions stored in thememory for carrying out the methods described herein. Further, thememory stores certain data, e.g. in databases or other data stores. Forexample, FIG. 6 shows schematically storage in the memory 218 of windforecasting engine software 260 including a physical, mathematical orother model and data processing logic 270 for determining wind forecastsin accordance with the present invention, a weather data data store 280for storing weather data received by the WFS and used by the forecastingengine, and a control signal engine 290 comprising instructions fortransmitting a control signal indicating the forecasted wind speeds,instructions for transmitting a control signal for controlling operationof a dependent system as a function of the forecasted wind speeds,instructions for causing the transmitter to transmit a control signalfor issuing a wind speed alert, and/or instructions for causing thedisplay to display the forecasted wind speeds for the geographicalregion during the future period of time. Optionally, other instructionsand/or data may be stored in the memory as discussed herein, such asinstructions to issue a control signal for controlling wind turbines'rotational speed, blade angle, or other operational aspects as afunction of forecasted wind or direction obtained from the presentinvention. The memory may further store data for displaying and/orcommunicating wind forecasts, control signals, and the like, asdiscussed herein.

Additionally, computer readable media storing computer readable code forcarrying out the method steps identified above is provided. The computerreadable media stores code for carrying out subprocesses for carryingout the methods described above.

A computer program product recorded on a computer readable medium forcarrying out the method steps identified above is provided. The computerprogram product comprises computer readable means for carrying out themethods described above.

Having thus described a few particular embodiments of the invention,various alterations, modifications, and improvements will readily occurto those skilled in the art. Such alterations, modifications, andimprovements as are made obvious by this disclosure are intended to bepart of this description though not expressly stated herein, and areintended to be within the spirit and scope of the invention.Accordingly, the foregoing description is by way of example only, andnot limiting. The invention is limited only as defined in the followingclaims and equivalents thereto.

1. A computer-implemented method for performing wind forecasting using acomputer-implemented wind forecasting system comprising amicroprocessor, a memory operatively coupled to the microprocessor, andmicroprocessor-executable instructions for causing the wind forecastingsystem to perform the wind forecasting method, the method comprising:storing, at the wind forecasting system, a model correlating large-scaleatmospheric pressure forces to time-dependent horizontal wind speed anddirection for a particular geographical region; receiving, at the windforecasting system, data reflecting a time-dependent horizontal windvelocity vector for the geographical region during a preceding period oftime, the preceding period of time preceding a reference time;determining, at the wind forecasting system, a trend reflecting changesover time of large-scale atmospheric pressure forces for thegeographical region during the preceding period of time as a function ofthe time-dependent wind velocity vector, according to the model;extrapolating, at the wind forecasting system, the trend in large-scaleatmospheric pressure forces for the preceding period of time todetermine forecasted large-scale atmospheric pressure forces for afuture period of time, the future period of time being a period of timesucceeding the reference time; and determining, at the wind forecastingsystem, a forecasted time-dependent horizontal wind velocity vector forthe geographical region during the future period of time, as a functionof the forecasted large-scale atmospheric pressure forces for the futureperiod of time, according to the model.
 2. The method of claim 1,further comprising: transmitting, from the wind forecasting system, acontrol signal for controlling operation of a dependent system as afunction of the forecasted time-dependent horizontal wind velocityvector during the future period of time; whereby operation of thedependent system is controlled at least in part by a short-term windforecast determined by the wind forecasting system.
 3. The method ofclaim 1, wherein the dependent system comprises a computerized deviceincluding a display for displaying a wind speed alert, and wherein thecontrol signal causes the display to display the forecastedtime-dependent horizontal wind speed and direction corresponding to thewind velocity vector.
 4. The method of claim 1, wherein the dependentsystem comprises a computerized device for issuing a wind speed alert,and wherein the control signal causes the device to issue the wind speedalert.
 5. The method of claim 4, wherein the dependent system comprisesa display, and wherein the control signal causes the display to displaythe forecasted time-dependent horizontal wind velocity vector.
 6. Themethod of claim 4, wherein the dependent system comprises a transmitter,and wherein the control signal causes the transmitter to transmit acontrol signal indicating the forecasted time-dependent horizontal windvelocity vector.
 7. The method of claim 4, wherein the dependent systemcomprises a transmitter, and wherein the control signal causes thetransmitter to selectively transmit a control signal as a function ofthe forecasted time-dependent horizontal wind velocity vector.
 8. Themethod of claim 4, wherein the dependent system comprises a powergeneration system, and wherein the control signal causes the powergeneration system to increase or decrease output from electrical powergeneration equipment as a function of the forecasted time-dependenthorizontal wind velocity vector.
 9. The method of claim 1, wherein thelarge-scale atmospheric pressure forces are expressed as a geostrophicwind.
 10. The method of claim 1, wherein the preceding period comprisesa short-term period of time of not more than 6 hours.
 11. The method ofclaim 10, wherein the preceding period comprises a short-term period oftime of not more than 3 hours.
 12. The method of claim 11, wherein thefuture period comprises a short-term period of time of not more than 6hours.
 13. The method of claim 12, wherein the future period comprises ashort-term period of time of not more than 3 hours.
 14. Acomputer-implemented method for performing wind forecasting using acomputer-implemented wind forecasting system comprising amicroprocessor, a memory operatively coupled to the microprocessor, andmicroprocessor-executable instructions for causing the wind forecastingsystem to perform the wind forecasting method, the method comprising:storing, at the wind forecasting system, a mathematical model expressingan imbalance among an atmospheric pressure gradient, Coriolis force andturbulent frictional stresses resulting from turbulent air flow mixingin earth's atmosphere for a particular geographical region to changesover time in Zonal and Meridional horizontal wind speeds for thegeographical region; receiving, at the wind forecasting system, datareflecting changes over time of Zonal and Meridional horizontal windspeeds for the geographical region during a preceding period of time,the preceding period of time preceding a reference time; performing, atthe wind forecasting system, an inverse application of the model todetermine, at the wind forecasting system, a trend reflecting changesover time of large-scale atmospheric pressure forces for thegeographical region during the preceding period of time as a function ofthe changes over time in Zonal and Meridional horizontal wind speeds forthe geographical region, according to the model, the trend includingforecasted large-scale atmospheric pressure forces for a future periodof time, the future period of time being a short-term period of timesucceeding the reference time; and performing, at the wind forecastingsystem, a forward application of the model to determine, at the windforecasting system, forecasted Zonal and Meridional horizontal windspeeds for the geographical region during the future period of time, asa function of the forecasted large-scale atmospheric pressure forces forthe future period of time, according to the model.
 15. The method ofclaim 14, wherein the mathematical model comprises equations of anunsteady atmospheric boundary layer relating changes over time in theZonal and Meridional wind speeds to the imbalance between the pressuregradient force, the Coriolis force and the friction force.
 16. Themethod of claim 14, wherein the mathematical model comprises theexpression:${{\frac{d^{2}A}{{dt}^{2}} + {\alpha \frac{dA}{dt}} + {{f_{c}\left( {f_{c} - {i\alpha}} \right)}A}} = {{f_{c}^{2}A_{g}} + {{if}_{c}\frac{dA_{g}}{dt}}}},$where A is the time-dependent horizontal wind velocity vector expressedas a complex variable (A=U+I V, where U and V are the Zonal andMeridional horizontal wind speed components, respectively) and A_(g) isa geostrophic value reflecting large scale atmospheric pressure gradientforce; f_(c) is the Coriolis force parameter; and α is a quantificationof turbulent stress resulting from eddy mixing, or an equivalentexpression expressing the balance of the pressure gradient force, theCoriolis force and the turbulent stress as an ordinary differentialequation.
 17. The method of claim 16, wherein α is determined as:${{\alpha \left( {z,t} \right)} = {{{- \frac{1}{U}}\frac{\partial}{\partial z}\left( {v_{T}\frac{\partial U}{\partial z}} \right)} = {{- \frac{1}{V}}\frac{\partial}{\partial z}\left( {v_{T}\frac{\partial V}{\partial z}} \right)}}},$where z is the elevation above ground at which the wind velocity vectoris sought, z₀ is the roughness length of the earth surface underneath, Uand V are the Zonal and Meridional wind speeds, and v_(T) the turbulenteddy viscosity.
 18. A computer-implemented method for performingshort-term wind forecasting using a computer-implemented windforecasting system comprising a microprocessor, a memory operativelycoupled to the microprocessor, and microprocessor-executableinstructions for causing the wind forecasting system to perform the windforecasting method, the method comprising: storing, at the windforecasting system, a physical model of time-varying wind flow inearth's atmosphere, the model correlating atmospheric pressure forces towind velocities over time; receiving, at the wind forecasting system,data reflecting changes over time of wind velocities for a particulargeographical region during a preceding period of time, the precedingperiod of time preceding a reference time; performing, at the windforecasting system, an inverse application of the model to determine, atthe wind forecasting system, a trend reflecting changes over time oflarge-scale atmospheric pressure forces for the geographical regionduring the preceding period of time as a function of the changes overtime in wind velocities for the geographical region; extrapolating, atthe wind forecasting system, the trend to determine forecastedlarge-scale atmospheric pressure forces for the geographical regionduring a future period of time, the future period of time being ashort-term period of time succeeding the reference time; and performing,at the wind forecasting system, a forward application of the model todetermine, at the wind forecasting system, forecasted wind speeds anddirections for the geographical region during the future period of time,as a function of the forecasted large-scale atmospheric pressure forcesfor the future period of time. 19-26. (canceled)